LiDPM: Rethinking Point Diffusion for Lidar Scene Completion (2504.17791v1)

Abstract: Training diffusion models that work directly on lidar points at the scale of outdoor scenes is challenging due to the difficulty of generating fine-grained details from white noise over a broad field of view. The latest works addressing scene completion with diffusion models tackle this problem by reformulating the original DDPM as a local diffusion process. It contrasts with the common practice of operating at the level of objects, where vanilla DDPMs are currently used. In this work, we close the gap between these two lines of work. We identify approximations in the local diffusion formulation, show that they are not required to operate at the scene level, and that a vanilla DDPM with a well-chosen starting point is enough for completion. Finally, we demonstrate that our method, LiDPM, leads to better results in scene completion on SemanticKITTI. The project page is https://astra-vision.github.io/LiDPM .

• The paper proposes LiDPM, a novel global diffusion process extending DDPMs for lidar scene completion, initialized from an intermediate noisy point for improved detail preservation and synthesis.
• LiDPM significantly outperforms prior diffusion methods, including LiDiff, on the SemanticKITTI dataset across metrics like Jensen-Shannon divergence, voxel IoU, and Chamfer distance.
• LiDPM's advancements improve autonomous driving perception and suggest new uses for vanilla DDPMs in unconditional generation and data augmentation for point cloud modeling.

Overview of LiDPM: Rethinking Point Diffusion for Lidar Scene Completion

The paper "LiDPM: Rethinking Point Diffusion for Lidar Scene Completion" presents a novel approach to the challenge of lidar scene completion using diffusion models. The authors propose a method that revisits point diffusion processes and extends denoising diffusion probabilistic models (DDPMs) to work at the scene level for outdoor lidar data. This approach contrasts with the prevailing paradigm of local diffusion processes and suggests that vanilla DDPMs, when appropriately initialized, can successfully tackle lidar scene completion tasks.

Methodology

The paper begins by acknowledging the strengths and limitations of existing diffusion processes applied to large lidar point clouds, primarily focusing on local diffusion techniques like LiDiff. Local diffusion models have previously been employed to scale diffusion processes to extensive scenes by spatially partitioning data, with notable simplifications to manage the complex data. However, LiDPM challenges this approach by arguing that these simplifications, such as local point denoising, may introduce unnecessary approximations which complicate and potentially limit performance.

LiDPM's core proposition is a global diffusion process that leverages the original DDPM framework augmented by strategic design choices. The authors suggest initiating the reverse diffusion process from an intermediate noisy point rather than complete Gaussian noise. This initialization strategy significantly improves the preservation of details inherent in the sparse lidar input while simultaneously enhancing the synthesis of missing scene parts.

A salient feature of the LiDPM method is its conditioning mechanism using classifier-free guidance. The neural network is trained to predict noise conditioned and unconditioned on the sparse point cloud data. This process allows the generated dense point clouds to maintain alignment with input data structures, leading to improved completion outcomes.

Results and Evaluation

The paper presents extensive evaluations on the SemanticKITTI dataset, underscoring the effectiveness of LiDPM against existing methods. LiDPM is shown to outperform prior diffusion models, including LiDiff, across various metrics such as Jensen-Shannon divergence, voxel IoU, and Chamfer distance. It demonstrates superior accuracy in synthesizing realistic, complete scenes from sparse lidar inputs.

The authors also provide detailed ablation studies, investigating the impact of different diffusion starting points and showcasing the flexibility of their approach. By evaluating the effect of different hyperparameters and solver steps, LiDPM establishes itself as robust and adaptable within the scene completion scope.

Implications and Future Perspectives

Practically, LiDPM's advancements hold significant implications for autonomous driving technology, where lidar-based sensing is integral. By improving the fidelity and completeness of environmental perception, LiDPM contributes directly to safer and more reliable vehicular navigation systems.

Theoretically, this paper opens pathways for revisiting assumptions in diffusion-based scene modeling. The possibility of using DDPMs for unconditional generation, as demonstrated by LiDPM, suggests potential applications beyond scene completion, including data augmentation and synthetic dataset creation, which are essential for training autonomous systems in varied conditions.

Going forward, potential developments could focus on further refining the initialization schemes or exploring alternative conditioning methods that could amplify the precision and computational efficiency of the diffusion process. Additionally, the paper paves the way for comparative studies into latent vs. point-based diffusion frameworks, highlighting a deeper understanding of how noise distributions can be leveraged to enrich generative point cloud models.

In summary, the LiDPM approach reinvigorates interest in vanilla DDPM frameworks, offering a compelling alternative for lidar scene completion that balances computational complexity with operational precision.